arXiv:2504.05294v3 Announce Type: replace Abstract: Chain-of-thought explanations are widely used to inspect the decision process of large language models (LLMs) and to evaluate the trustworthiness of model outputs, making them important for effective collaboration between LLMs and humans. We demonstrate that preference optimization - a key step in the alignment phase - can inadvertently reduce the faithfulness of these explanations. This occurs because the reward model (RM), which guides alignment, is tasked with optimizing both the expected quality of the response and the appropriateness of
The rapid deployment and increasing reliance on large language models (LLMs) across various sectors necessitates robust methodologies for ensuring their explainability and trustworthiness, making research into their internal mechanisms critical today.
This research highlights a fundamental challenge in aligning AI systems: optimizing for desired outcomes can inadvertently compromise the faithfulness of AI explanations, potentially leading to flawed decision-making when humans collaborate with LLMs.
Our understanding of AI alignment strategies is deepened by identifying a new failure mode where current preference optimization techniques can reduce the transparency and reliability of chain-of-thought explanations.
- · AI safety researchers
- · Developers of robust AI alignment techniques
- · Organizations prioritizing AI transparency
- · Users relying solely on current LLM explanations
- · Models optimized only for short-term performance metrics
- · Current reward model architectures
Increased scrutiny and demand for more sophisticated reward models and alignment techniques that preserve explanatory faithfulness in LLMs.
A potential slowdown in the adoption of AI systems in highly sensitive domains if explainability concerns remain unaddressed or if mitigation proves complex.
The development of a new sub-field focused on 'causal attribution for AI explainability' as a core component of trusted AI development frameworks.
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